Aliases: knn.cv
Keywords: classif nonparametric
### ** Examples data(iris3) train <- rbind(iris3[,,1], iris3[,,2], iris3[,,3]) cl <- factor(c(rep("s",50), rep("c",50), rep("v",50))) knn.cv(train, cl, k = 3, prob = TRUE)
[1] s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s s
[38] s s s s s s s s s s s s s c c c c c c c c c c c c c c c c c c c c v c v c
[75] c c c c c c c c c v c c c c c c c c c c c c c c c c v v v v v v c v v v v
[112] v v v v v v v v c v v v v v v v v v v v v v c v v v v v v v v v v v v v v
[149] v v
attr(,"prob")
[1] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[8] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[15] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[22] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[29] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[36] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[43] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[50] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[57] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[64] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.6666667 1.0000000
[71] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[78] 0.6666667 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[85] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[92] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[99] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[106] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 0.6666667 1.0000000
[113] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[120] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[127] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[134] 0.6666667 0.6666667 1.0000000 1.0000000 1.0000000 0.6666667 1.0000000
[141] 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
[148] 1.0000000 1.0000000 1.0000000
attr(,"nn.index")
[,1] [,2] [,3]
[1,] 18 5 40
[2,] 35 46 13
[3,] 48 4 7
[4,] 48 30 31
[5,] 38 1 18
[6,] 19 11 49
[7,] 48 3 12
[8,] 40 50 1
[9,] 39 4 43
[10,] 35 2 31
[11,] 49 28 37
[12,] 30 8 27
[13,] 2 10 46
[14,] 39 43 9
[15,] 34 17 16
[16,] 34 15 6
[17,] 11 49 34
[18,] 1 41 5
[19,] 6 11 49
[20,] 22 47 49
[21,] 32 28 29
[22,] 20 47 18
[23,] 7 3 38
[24,] 27 44 40
[25,] 12 30 27
[26,] 35 10 2
[27,] 24 44 8
[28,] 29 1 40
[29,] 28 40 1
[30,] 31 4 12
[31,] 30 35 10
[32,] 21 28 29
[33,] 34 47 20
[34,] 33 16 17
[35,] 10 2 31
[36,] 50 2 3
[37,] 11 32 29
[38,] 5 1 41
[39,] 9 43 14
[40,] 8 1 28
[41,] 18 1 5
[42,] 9 39 46
[43,] 39 48 4
[44,] 27 24 22
[45,] 47 6 22
[46,] 2 13 35
[47,] 20 22 49
[48,] 4 3 43
[49,] 11 28 20
[50,] 8 40 36
[51,] 53 87 66
[52,] 57 76 66
[53,] 51 87 78
[54,] 90 81 70
[55,] 59 76 77
[56,] 67 91 97
[57,] 52 86 92
[58,] 94 99 61
[59,] 76 55 66
[60,] 90 95 54
[61,] 94 58 82
[62,] 97 79 96
[63,] 93 70 68
[64,] 92 74 79
[65,] 83 80 89
[66,] 76 59 87
[67,] 85 56 97
[68,] 93 83 100
[69,] 88 73 120
[70,] 81 90 82
[71,] 139 128 150
[72,] 98 83 93
[73,] 134 124 147
[74,] 64 92 79
[75,] 98 76 59
[76,] 66 59 75
[77,] 59 87 53
[78,] 53 87 148
[79,] 92 64 62
[80,] 82 81 70
[81,] 82 70 90
[82,] 81 70 80
[83,] 93 100 68
[84,] 134 102 143
[85,] 67 56 97
[86,] 57 71 52
[87,] 53 66 59
[88,] 69 73 63
[89,] 96 97 100
[90,] 54 70 81
[91,] 95 56 97
[92,] 64 79 74
[93,] 83 68 100
[94,] 58 61 99
[95,] 100 97 91
[96,] 97 89 100
[97,] 96 100 89
[98,] 75 72 92
[99,] 58 94 61
[100,] 97 95 89
[101,] 137 145 105
[102,] 143 114 122
[103,] 126 121 144
[104,] 117 138 129
[105,] 133 129 141
[106,] 123 108 136
[107,] 85 60 91
[108,] 131 126 106
[109,] 129 104 117
[110,] 144 121 145
[111,] 148 116 78
[112,] 148 129 147
[113,] 140 141 121
[114,] 143 102 122
[115,] 122 102 143
[116,] 149 111 146
[117,] 138 104 148
[118,] 132 106 110
[119,] 123 106 136
[120,] 73 84 69
[121,] 144 141 125
[122,] 143 102 114
[123,] 106 119 108
[124,] 127 147 128
[125,] 121 144 141
[126,] 130 103 108
[127,] 124 128 139
[128,] 139 127 150
[129,] 133 105 104
[130,] 126 131 103
[131,] 108 103 126
[132,] 118 106 136
[133,] 129 105 104
[134,] 84 73 124
[135,] 104 84 134
[136,] 131 106 103
[137,] 149 116 101
[138,] 117 104 148
[139,] 128 71 127
[140,] 113 146 142
[141,] 145 121 113
[142,] 146 140 113
[143,] 143 114 122
[144,] 121 125 145
[145,] 141 121 144
[146,] 142 148 140
[147,] 124 112 127
[148,] 111 112 117
[149,] 137 116 111
[150,] 128 139 102
attr(,"nn.dist")
[,1] [,2] [,3]
[1,] 0.1000000 0.1414214 0.1414214
[2,] 0.1414214 0.1414214 0.1414214
[3,] 0.1414214 0.2449490 0.2645751
[4,] 0.1414214 0.1732051 0.2236068
[5,] 0.1414214 0.1414214 0.1732051
[6,] 0.3316625 0.3464102 0.3605551
[7,] 0.2236068 0.2645751 0.3000000
[8,] 0.1000000 0.1414214 0.1732051
[9,] 0.1414214 0.3000000 0.3162278
[10,] 0.1000000 0.1732051 0.1732051
[11,] 0.1000000 0.2828427 0.3000000
[12,] 0.2236068 0.2236068 0.2828427
[13,] 0.1414214 0.1732051 0.2000000
[14,] 0.2449490 0.3162278 0.3464102
[15,] 0.4123106 0.4690416 0.5477226
[16,] 0.3605551 0.5477226 0.6164414
[17,] 0.3464102 0.3605551 0.3872983
[18,] 0.1000000 0.1414214 0.1732051
[19,] 0.3316625 0.3872983 0.4690416
[20,] 0.1414214 0.1414214 0.2449490
[21,] 0.2828427 0.3000000 0.3605551
[22,] 0.1414214 0.2449490 0.2449490
[23,] 0.4582576 0.5099020 0.5099020
[24,] 0.2000000 0.2645751 0.3741657
[25,] 0.3000000 0.3741657 0.4123106
[26,] 0.1732051 0.2000000 0.2236068
[27,] 0.2000000 0.2236068 0.2236068
[28,] 0.1414214 0.1414214 0.1414214
[29,] 0.1414214 0.1414214 0.1414214
[30,] 0.1414214 0.1732051 0.2236068
[31,] 0.1414214 0.1414214 0.1732051
[32,] 0.2828427 0.3000000 0.3000000
[33,] 0.3464102 0.3464102 0.3741657
[34,] 0.3464102 0.3605551 0.3872983
[35,] 0.1000000 0.1414214 0.1414214
[36,] 0.2236068 0.3000000 0.3162278
[37,] 0.3000000 0.3162278 0.3316625
[38,] 0.1414214 0.2449490 0.2645751
[39,] 0.1414214 0.2000000 0.2449490
[40,] 0.1000000 0.1414214 0.1414214
[41,] 0.1414214 0.1732051 0.1732051
[42,] 0.6244998 0.7141428 0.7681146
[43,] 0.2000000 0.2236068 0.3000000
[44,] 0.2236068 0.2645751 0.3162278
[45,] 0.3605551 0.3741657 0.4123106
[46,] 0.1414214 0.2000000 0.2000000
[47,] 0.1414214 0.2449490 0.2449490
[48,] 0.1414214 0.1414214 0.2236068
[49,] 0.1000000 0.2236068 0.2449490
[50,] 0.1414214 0.1732051 0.2236068
[51,] 0.2645751 0.3316625 0.4358899
[52,] 0.2645751 0.3162278 0.3464102
[53,] 0.2645751 0.2828427 0.3162278
[54,] 0.2000000 0.3000000 0.3162278
[55,] 0.2449490 0.3162278 0.3741657
[56,] 0.3000000 0.3162278 0.3162278
[57,] 0.2645751 0.3741657 0.4242641
[58,] 0.1414214 0.3872983 0.4582576
[59,] 0.2449490 0.2449490 0.3162278
[60,] 0.3872983 0.5099020 0.5196152
[61,] 0.3605551 0.4582576 0.6708204
[62,] 0.3000000 0.3316625 0.3605551
[63,] 0.4898979 0.5196152 0.5477226
[64,] 0.1414214 0.2236068 0.2449490
[65,] 0.4242641 0.4472136 0.5099020
[66,] 0.1414214 0.3162278 0.3162278
[67,] 0.2000000 0.3000000 0.3872983
[68,] 0.2449490 0.2828427 0.3316625
[69,] 0.2645751 0.5099020 0.5385165
[70,] 0.1732051 0.2449490 0.2645751
[71,] 0.2236068 0.3000000 0.3605551
[72,] 0.3316625 0.3464102 0.3741657
[73,] 0.3605551 0.3605551 0.4123106
[74,] 0.2236068 0.3000000 0.3872983
[75,] 0.2000000 0.2645751 0.3605551
[76,] 0.1414214 0.2449490 0.2645751
[77,] 0.3162278 0.3464102 0.3464102
[78,] 0.3162278 0.3741657 0.4123106
[79,] 0.2000000 0.2449490 0.3316625
[80,] 0.3464102 0.4242641 0.4358899
[81,] 0.1414214 0.1732051 0.3000000
[82,] 0.1414214 0.2645751 0.3464102
[83,] 0.1414214 0.2645751 0.2828427
[84,] 0.3316625 0.3605551 0.3605551
[85,] 0.2000000 0.4123106 0.4795832
[86,] 0.3741657 0.4242641 0.4582576
[87,] 0.2828427 0.3162278 0.3162278
[88,] 0.2645751 0.5744563 0.5916080
[89,] 0.1732051 0.1732051 0.2236068
[90,] 0.2000000 0.2449490 0.3000000
[91,] 0.2645751 0.3162278 0.4242641
[92,] 0.1414214 0.2000000 0.3000000
[93,] 0.1414214 0.2449490 0.2645751
[94,] 0.1414214 0.3605551 0.3872983
[95,] 0.1732051 0.2236068 0.2645751
[96,] 0.1414214 0.1732051 0.2449490
[97,] 0.1414214 0.1414214 0.1732051
[98,] 0.2000000 0.3316625 0.3464102
[99,] 0.3872983 0.3872983 0.7211103
[100,] 0.1414214 0.1732051 0.2236068
[101,] 0.4242641 0.5000000 0.5099020
[102,] 0.0000000 0.2645751 0.3162278
[103,] 0.3872983 0.4000000 0.4123106
[104,] 0.2449490 0.2449490 0.3316625
[105,] 0.3000000 0.3162278 0.3605551
[106,] 0.2645751 0.5291503 0.5477226
[107,] 0.7348469 0.7615773 0.7937254
[108,] 0.2645751 0.4358899 0.5291503
[109,] 0.5567764 0.6000000 0.6164414
[110,] 0.6324555 0.6708204 0.7071068
[111,] 0.2236068 0.3741657 0.4242641
[112,] 0.3464102 0.3741657 0.3741657
[113,] 0.1732051 0.3464102 0.3605551
[114,] 0.2645751 0.2645751 0.3316625
[115,] 0.4898979 0.5099020 0.5099020
[116,] 0.3000000 0.3741657 0.3741657
[117,] 0.1414214 0.2449490 0.3605551
[118,] 0.4123106 0.8185353 0.8602325
[119,] 0.4123106 0.5477226 0.8944272
[120,] 0.4358899 0.5196152 0.5385165
[121,] 0.2236068 0.2645751 0.3000000
[122,] 0.3162278 0.3162278 0.3316625
[123,] 0.2645751 0.4123106 0.6082763
[124,] 0.1732051 0.2449490 0.3605551
[125,] 0.3000000 0.3162278 0.3741657
[126,] 0.3464102 0.3872983 0.4358899
[127,] 0.1732051 0.2449490 0.2828427
[128,] 0.1414214 0.2449490 0.2828427
[129,] 0.1000000 0.3162278 0.3316625
[130,] 0.3464102 0.5099020 0.5196152
[131,] 0.2645751 0.4582576 0.4690416
[132,] 0.4123106 0.8831761 0.9273618
[133,] 0.1000000 0.3000000 0.4242641
[134,] 0.3316625 0.3605551 0.3741657
[135,] 0.5385165 0.5567764 0.5830952
[136,] 0.5385165 0.5477226 0.6633250
[137,] 0.2449490 0.3872983 0.4242641
[138,] 0.1414214 0.2449490 0.3872983
[139,] 0.1414214 0.2236068 0.2828427
[140,] 0.1732051 0.3605551 0.3605551
[141,] 0.2449490 0.2645751 0.3464102
[142,] 0.2449490 0.3605551 0.4690416
[143,] 0.0000000 0.2645751 0.3162278
[144,] 0.2236068 0.3162278 0.3162278
[145,] 0.2449490 0.3000000 0.3162278
[146,] 0.2449490 0.3605551 0.3605551
[147,] 0.2449490 0.3741657 0.3872983
[148,] 0.2236068 0.3464102 0.3605551
[149,] 0.2449490 0.3000000 0.5567764
[150,] 0.2828427 0.3162278 0.3316625
Levels: c s v
attributes(.Last.value)
NULL